A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

Abstract EN

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task.

Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy.

However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors.

To deal with these problems, an improved dynamic BINN is proposed in this paper.

In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors.

Then the BINN will move with the AUV and the computing could be reduced.

A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically.

Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities.

Finally, some experiments are conducted under various 3D underwater environments.

The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.